We propose and develop mean-variance-ratio (MVR) statistics for comparing the performance of prospects (e.g., investment portfolios, assets, etc.) after the effect of the background risk has been mitigated. We investigate the performance of the statistics in large and small samples and show that...

In this paper, we propose a quick and efficient method to examine whether a time series Yt possesses any nonlinear feature by testing a kind of dependence remained in the residuals after fitting Yt with a linear model. The advantage of our proposed nonlinearity test is that it is not required to...

The famous Hiemstra-Jones test (HJ test) developed by Hiemstra and Jones plays a significant role in studying nonlinear causality. In the last two decades there are numerous applications and theoretical extensions based on this pioneering work. However, several works pointed out that...

We propose and develop mean-variance-ratio (MVR) statistics for comparing the performance of prospects (e.g., investment portfolios, assets, etc.) after the effect of the background risk has been mitigated. We investigate the performance of the statistics in large and small samples and show that...

In this article we propose a quick, efficient, and easy method to detect whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. Our proposed...

This paper considers the portfolio problem for high dimensional data when the dimension and size are both large.We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main factor to...